INRIASAC: Simple Hypernym Extraction Methods

Given a set of terms from a given domain, how can we structure them into a taxonomy without manual intervention? This is the task 17 of SemEval 2015. Here we present our simple taxonomy structuring techniques which, despite their simplicity, ranked first in this 2015 benchmark. We use large quantities of text (English Wikipedia) and simple heuristics such as term overlap and document and sentence co-occurrence to produce hypernym lists. We describe these techniques and pre-sent an initial evaluation of results.Comment: SemEval 2015, Jun 2015, Denver, United State

Document Filtering for Long-tail Entities

Filtering relevant documents with respect to entities is an essential task in the context of knowledge base construction and maintenance. It entails processing a time-ordered stream of documents that might be relevant to an entity in order to select only those that contain vital information. State-of-the-art approaches to document filtering for popular entities are entity-dependent: they rely on and are also trained on the specifics of differentiating features for each specific entity. Moreover, these approaches tend to use so-called extrinsic information such as Wikipedia page views and related entities which is typically only available only for popular head entities. Entity-dependent approaches based on such signals are therefore ill-suited as filtering methods for long-tail entities. In this paper we propose a document filtering method for long-tail entities that is entity-independent and thus also generalizes to unseen or rarely seen entities. It is based on intrinsic features, i.e., features that are derived from the documents in which the entities are mentioned. We propose a set of features that capture informativeness, entity-saliency, and timeliness. In particular, we introduce features based on entity aspect similarities, relation patterns, and temporal expressions and combine these with standard features for document filtering. Experiments following the TREC KBA 2014 setup on a publicly available dataset show that our model is able to improve the filtering performance for long-tail entities over several baselines. Results of applying the model to unseen entities are promising, indicating that the model is able to learn the general characteristics of a vital document. The overall performance across all entities---i.e., not just long-tail entities---improves upon the state-of-the-art without depending on any entity-specific training data.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 201

Class attendance, peer similarity, and academic performance in a large field study

Identifying the factors that determine academic performance is an essential part of educational research. Existing research indicates that class attendance is a useful predictor of subsequent course achievements. The majority of the literature is, however, based on surveys and self-reports, methods which have well-known systematic biases that lead to limitations on conclusions and generalizability as well as being costly to implement. Here we propose a novel method for measuring class attendance that overcomes these limitations by using location and bluetooth data collected from smartphone sensors. Based on measured attendance data of nearly 1,000 undergraduate students, we demonstrate that early and consistent class attendance strongly correlates with academic performance. In addition, our novel dataset allows us to determine that attendance among social peers was substantially correlated ($>$0.5), suggesting either an important peer effect or homophily with respect to attendance

Independent associations between sedentary time, moderate-to-vigorous physical activity, cardiorespiratory fitness and cardio-metabolic health : a cross-sectional study

We aimed to study the independent associations of sedentary time (ST), moderate-to-vigorous physical activity (MVPA), and objectively measured cardiorespiratory fitness (CRF) with clustered cardio-metabolic risk and its individual components (waist circumference, fasting glucose, HDL-cholesterol, triglycerides and blood pressure). We also investigated whether any associations between MVPA or ST and clustered cardio-metabolic risk were mediated by CRF. MVPA, ST, CRF and individual cardio-metabolic components were measured in a population-based sample of 341 adults (age 53.8 +/- 8.9 years; 61% men) between 2012 and 2014. MVPA and ST were measured with the SenseWear pro 3 Armband and CRF was measured with a maximal exercise test. Multiple linear regression models and the product of coefficients method were used to examine independent associations and mediation effects, respectively. Results showed that low MVPA and low CRF were associated with a higher clustered cardio-metabolic risk (beta = -0.26 and beta = -0.43, both p<0.001, respectively). CRF explained 73% of the variance in the association between MVPA and clustered cardio-metabolic risk and attenuated this association to non-significance. After mutual adjustment for MVPA and ST, CRF was the most important risk factor for a higher clustered cardio-metabolic risk (beta = -0.39, p<0.001). In conclusion, because of the mediating role of CRF, lifestyle-interventions need to be feasible yet challenging enough to lead to increases in CRF to improve someone's cardio-metabolic health

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment

The Presence and Consequences of Abortion Aversion in Scientific Research Related to Alcohol Use during Pregnancy.

Recent research has found that most U.S. state policies related to alcohol use during pregnancy adversely impact health. Other studies indicate that state policymaking around substance use in pregnancy-especially in the U.S.-appears to be influenced by an anti-abortion agenda rather than by public health motivations. This commentary explores the ways that scientists' aversion to abortion appear to influence science and thus policymaking around alcohol and pregnancy. The three main ways abortion aversion shows up in the literature related to alcohol use during pregnancy include: (1) a shift from the recommendation of abortion for "severely chronic alcoholic women" to the non-acknowledgment of abortion as an outcome of an alcohol-exposed pregnancy; (2) the concern that recommendations of abstinence from alcohol use during pregnancy lead to terminations of otherwise wanted pregnancies; and (3) the presumption of abortion as a negative pregnancy outcome. Thus, abortion aversion appears to influence the science related to alcohol use during pregnancy, and thus policymaking-to the detriment of developing and adopting policies that reduce the harms from alcohol during pregnancy

"Ought Implies Can,” Framing Effects, and "Empirical Refutations"

This paper aims to contribute to the current debate about the status of the “Ought Implies Can” principle and the growing body of empirical evidence that undermines it. We report the results of an experimental study which show that people judge that agents ought to perform an action even when they also judge that those agents cannot do it and that such “ought” judgments exhibit an actor-observer effect. Because of this actor-observer effect on “ought” judgments and the Duhem-Quine thesis, talk of an “empirical refutation” of OIC is empirically and methodologically unwarranted. What the empirical fact that people attribute moral obligations to unable agents shows is that OIC is not intuitive, not that OIC has been refuted